Case Study: Analysis-By-Parts

Background:

Traditional analysis applies a change in demand or production for a sector in the input-output model. The direct effect is the value applied to the multipliers, the indirect effects are the inter-industry interactions, and the induced effects are the additional impacts caused by payroll as it is re-spent in the economy.

Analysis-by-parts (ABP), on the other hand, does not start with an impact on our target industry sector. Instead we will specify the goods and services the target industry purchases in order to satisfy a demand or production level. The purchase of these goods and services from local sources actually represents the first round of indirect purchases by the target industry. In addition to the goods and services (first part), we need to analyze the impact of the payroll (second part) of our target industry necessary to meet the new demand or production level.

The purpose of this case study is to show that an analysis-by-parts (task B) can yield the same results as the traditional analysis (task A). Then we will demonstrate a more realistic example of when an analysis-by-parts is useful (task C).

Task A. Traditional analysis:

We wish to find the economic impact caused by a $2,000,000 expansion in Windows and Doors manufacturing. We will conduct the analysis first as a "traditional" analysis and will use the Washington County, MN 2007 demo data file found in every installation of IMPLAN Pro.

Steps:

Create the IMPLAN Pro model and build through multipliers.

Set the default year in user preferences to 2007: File > User Preferences > Analysis (tab) > and select "2007", then hit the "OK" button. This is so that when we analyze and compare the results for both tasks A and B, we do so without running afoul of deflators.

Click on the newly imported activity ("99 Wood windows and doors and millwork manufacturing") and notice:

The events are coefficients and sum to less than 1. The missing piece is the portion of the production function that goes to labor income and other value added.

The local purchase percentage ranges from 0 to 100%. In the traditional analysis, the LPP is 100% - that is all production of Wood windows and doors is local. We are now specifying as the "direct" effect the first round of spending in order to produce the windows and doors. We cannot assume that all those purchases come from local sources, and we let the software set the LPP for us. However, the analyst may change the LPPs if he has local information.

Set the activity level for the intermediate purchases to 2,000,000. Since the production function shows the goods and services inputs required for each dollar of output, we set the activity level to the new production total being modeled.

Click on the activity "99 Wood windows and doors and millwork manufacturing".

Click on "Edit Activity" and set the Activity Level field to 2,000,000 and save.

Create the Labor Income part of the ABP impact:

Setup Activity > New Activity > (Activity Type) Labor Income Change and name it "ABP Labor Income" and hit "Save".

In the event window, hit "New Event" and choose Sector "5001 Employee Compensation". Set the Labor Income Value to the direct employee compensation associated with 2,000,000 in production. The value can be seen in figure A1 above: $690,031.23.

Create and run the new ABP Scenario

Name the scenario "ABP" and include both the "ABP Labor Income" and the "99 Wood windows and doors and millwork manufacturing" activities.

Compare the results to the traditional analysis:

Figure B1

Comments:

First, notice that the indirect and induced effects for both the traditional analysis (figure A2) and the analysis-by-parts (figure B1) match.

Second, why are the direct effects zero? By definition, any impacts resulting from labor income are induced effects. When an Employee Compensation Labor Income type activity is created, the software automatically moves the resulting first round of spending by households (the "direct") to the induced effects in the results. The software also moves any "indirect" effects resulting from the first round of induced effects to the induced category.

Likewise, the first round of spending on goods and services is moved from the direct category to the indirect category in the results. For an Industry Spending Pattern, there will still be indirect labor income, which generates additional induced effects. These induced effects are added to the induced effect generated by the initial labor income.

In the ABP analysis, the logical direct effect is the same as the direct effects specified and applied to the multipliers in the traditional analysis (as shown in figure A2).

Task C. Example Analysis-by-parts:

Table C1. Operations spending per 100mW Wind Power

Sector

Coefficient

$2008 per 100 MW

3031

0.001050

34,616

3039

0.000315

10,385

3115

0.000525

17,308

3185

0.001161

38,250

3193

0.001127

37,125

3222

0.029916

985,885

3229

0.001127

37,125

3276

0.000673

22,190

3277

0.000673

22,190

3351

0.000053

1,731

3354

0.218534

7,201,820

3357

0.010100

332,844

3365

0.000053

1,731

3367

0.000053

1,731

3386

0.000053

1,731

Intermediate

0.265411

8,746,662

EC

0.013549

446,504

PI

0.000000

0

OPI

0.703555

23,185,784

IBT

0.017486

576,244

Value Added

0.734589

24,208,533

TIO

1

32,955,194

Employment

6

We want to demonstrate a typical example of an ABP. In this example, we will show the annual operational impact of a 500 megawatt wind farm. Sector 31 Electric Power Generation and Distribution has a national average production function for all types of power generation (wind, natural gas, oil, nuclear, hydro) as well as distribution.

I wish to take advantage of detailed spending patterns available in the NREL worksheets (http://www.nrel.gov/analysis/jedi/download.html ) to perform an analysis-by-parts to analyze the operational impacts with data specific to wind power.

Based on the data found in the worksheet, I have created the spending pattern shown below which is on a per 100 MegaWatt basis. The NREL spreadsheet indicates that the values are in $2008.

Using table C1 to the right and the Washington County model created in task A, we can perform the analysis by parts.

Steps:

Under setup activities, choose New Activity > Industry spending Pattern. Name the new activity "Wind Power spending per MW". Set the Activity Level to 32,955,194. Click "Save".

Create a new event by clicking the "New Event" button and then choosing commodity 3031 (the primary commodity produced by industry 31).

Change the LPP: Event Options > Edit Event Properties > Local Purchase Percentage > Set to SAM Model Value. We know that wind power is generated 100% locally, but we don't know from where the goods and services are purchased.

Create new events for each of the other sectors 3039 through 3386. Note that each of the subsequent events will have event year 2008 and an LPP that is not 100% (ie, will be based on the SAM model value). Also, one or more of the commodities will not exist locally,� so that value will not generate any local activity and the LPP will be 0. Check that the Sum of Event Values is 0.27.

Now we can create the Labor income activity. Choose New Activity > (type of activity) Labor Income Change. Name the activity "Wind Power Labor per 100 MW" and leave the Activity Level at 1.

The only new event will be 5001 Employee Compensation. Set the event year to 2008 before plugging in $446,504 for the Labor Income Value.

Run the analysis with a new scenario named "Wind Power 500 MW", setting the Scenario level to 5. Choose the two activities: "Wind Power spending per 100 MW" and "Wind Power Labor", then click "Analyze Single Region".

View the results with 2012 selected as the Dollar Year for View.

Figure C1

Discussion of Results:

We have indirect and induced effects. We still need to calculate the direct effects and add them to the table (based on values found in Table C1 and deflators found in the software).

As an analyst, it is important to look at what turns out to be the most import indirect effect - interest. Is this a local corporation borrowing from a local bank (total loan annual repayment of over $20 million), or is it a large outside corporation that happens to be locating here and borrowing from an out of region bank? Setting the LPP to 0 for interest would cancel the largest component of the indirect impact.